Monte Carlo Method
This page contains resources about Monte Carlo Methods, Sampling Methods,' Monte Carlo Inference', Stochastic Simulation, Systems Simulation and Computational Modelling. Subfields and Concepts * Monte Carlo techniques ** Particle Filtering / Sequential Monte Carlo (SMC) ** Kalman Filtering ** Importance Sampling ** Sequential Importance Sampling ** Rejection Sampling ** Rao-Blackwellised Particle Filtering (RBPF) ** Markov Chain Monte Carlo (MCMC) *** Gibbs Sampling *** Metropolis–Hastings (MH) Algorithm *** MH-in-Gibbs / Variable-at-a-time / Metropolis-within-Gibbs / MH-within-Gibbs *** Hybrid / Hamiltonian Monte Carlo (HMC) *** No-U-Turn Sampler (NUTS) ** Simulated Annealing ** Annealed Importance Sampling ** Cross-entropy (CE) Method * Variance Reductions Techniques (VRT) ** Antithetic Variables ** Control variates / Regression sampling ** Importance Sampling * Simulation and Computational Modelling ** Estimation Theory / Parameter Estimation ** Stochastic Optimization ** Model fitting ** Model selection and evaluation ** Uncertainty and Sensitivity Analysis Online Courses Video Lectures *Monte Carlo Simulation for Statistical Inference, Model Selection and Decision Making by Nando de Freitas - VideoLectures.NET *Sequential Monte-Carlo Methods by Arnaud Doucet and Nando de Freitas - VideoLectures.NET *MCMC Learning by Varun Kanade - VideoLectures.NET *Markov Chain Monte Carlo by Ian Murray - VideoLectures.NET Lecture Notes *Monte Carlo Methods by Michael Mascagni *Simulation Methods by Alan Genz *Stochastic Simulation by Elad Hazan *Systems Simulation by Hossein Arsham *Lecture: Parameter estimation, uncertainty, model fitting, model selection, and sensitivity and uncertainty analysis by Jamie Lloyd-Smith *Lecture: Model Fitting and Error Estimation by Kevin D. Costa Books and Book Chapters * Rubinstein, R. Y., & Kroese, D. P. (2016). Simulation and the Monte Carlo method. 3rd Ed. John Wiley & Sons. * Bengio, Y., Goodfellow, I. J., & Courville, A. (2016). "Chapter 17: Monte Carlo Methods". Deep Learning. MIT Press. * Theodoridis, S. (2015). "Chapter 14: Monte Carlo Methods". Machine Learning: A Bayesian and Optimization Perspective. Academic Press. * Theodoridis, S. (2015). "Chapter 17: Particle Filtering". Machine Learning: A Bayesian and Optimization Perspective. Academic Press. * Law, A. M., Kelton (2014). Simulation modeling and analysis. 5th Ed. McGraw-Hill. * Owen, A. B. (2013). Monte Carlo Theory, Methods and Examples. (link) * Durbin, J., & Koopman, S. J. (2012). Time series analysis by state space methods. Oxford University Press. * Murphy, K. P. (2012). "Chapter 23: Monte Carlo inference". Machine Learning: A Probabilistic Perspective. MIT Press. * Barber, D. (2012). "Chapter 27: Sampling". Bayesian Reasoning and Machine Learning. Cambridge University Press. * Ross, S. M. (2012). Simulation. 5th Ed. Academic Press. * Brooks, S., Gelman, A., Jones, G., & Meng, X. L. (Eds.). (2011). Handbook of Markov Chain Monte Carlo. CRC press. * Kroese, D. P., Taimre, T., & Botev, Z. I. (2011). Handbook of monte carlo methods. John Wiley & Sons. * Robert, C., & Casella, G. (2010). Monte Carlo statistical methods. Springer Science & Business Media. * Koller, D., & Friedman, N. (2009). "Chapter 12: Particle-Based Approximate Inference". Probabilistic Graphical Models. MIT Press. * Asmussen, S., & Glynn, P. W. (2007). Stochastic simulation: algorithms and analysis. Springer Science & Business Media. * Bishop, C. M. (2006). "Chapter 11: Sampling Methods". Pattern Recognition and Machine Learning. Springer. * MacKay, D. J. (2003). "Chapter 29: Monte Carlo Methods" Information Theory, Inference and Learning Algorithms. Cambridge University Press. * Rubinstein, R. Y., & Melamed, B. (1998). Modern simulation and modeling. John Wiley & Sons. * Gilks, W. R., Richardson, S. & Spiegelhalter, D. J. (eds): Markov Chain Monte Carlo in Practice. Chapman & Hall/CRC, 1996. Scholarly Articles * Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods. * MacKay, D. J. (1998). Introduction to monte carlo methods. In Learning in graphical models (pp. 175-204). Springer Netherlands. Tutorials *MCMC Review Software * MCMC toolbox - MATLAB * Ensemble MCMC sampler - MATLAB * emce - Python * PyMC3 - Python See also *Variational Methods, an other set of methods for Approximate Inference in Graphical Models *State Space Models *Kalman filter *Robot Localization *Computational Finance *Optimization Other Resources * MCMC sampling for dummies - Python * MCMC programming in R, Python, Java and C * Video lectures on Monte Carlo - VideoLectures.NET * Monte Carlo, and Other Kinds of Stochastic Simulation - Notebook Category:Probabilistic Graphical Models